A novel Invasive Weed Optimization with levy flight for optimization problems: The case of forecasting energy demand

Abstract

Energy is very important nowadays and it has become essential for human life. More amount of energy is necessary for a community with increased living standards and population. This causes an increase in energy consumption. Energy has become one of the most significant problems across the world today; therefore, it should be generated at the best level. Excessive energy generation makes countries lose money while less amount of energy generation causes crises. Therefore, countries need to adjust their energy demand optimally. It is possible to estimate energy demands of countries by using various applications. This study proposes a new improved algorithm for linear regression models to forecast the energy demand of Turkey. The selected algorithm is Invasive Weed Optimization (IWO) algorithm which has been developed with levy flight called LF-IWO. In the linear regression model, input parameters included data regarding Turkey’s gross domestic product (GDP), population, import and export. Turkey’s energy demand was estimated for these parameters by using the data between 1979 and 2011. The estimation results obtained from the model were compared with those of similar studies in the literature to measure the performance success of the developed algorithm